Interpretable modeling of time-resolved single-cell gene-protein expression with CrossmodalNet.

Cell-surface proteins play a critical role in cell function and are primary targets for therapeutics. CITE-seq is a single-cell technique that enables simultaneous measurement of gene and surface protein expression. It is powerful but costly and technically challenging. Computational methods have been developed to predict surface protein expression using gene expression information such as from single-cell RNA sequencing (scRNA-seq) data. Existing methods however are computationally demanding and lack the interpretability to reveal underlying biological processes. We propose CrossmodalNet, an interpretable machine learning model, to predict surface protein expression from scRNA-seq data. Our model with a customized adaptive loss accurately predicts surface protein abundances. When samples from multiple time points are given, our model encodes temporal information into an easy-to-interpret time embedding to make prediction in a time-point-specific manner, and is able to uncover noise-free causal gene-protein relationships. Using three publicly available time-resolved CITE-seq data sets, we validate the performance of our model by comparing it with benchmarking methods and evaluate its interpretability. Together, we show that our method accurately and interpretably profiles surface protein expression using scRNA-seq data, thereby expanding the capacity of CITE-seq experiments for investigating molecular mechanisms involving surface proteins.

Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.

In this paper, we introduce Gene Knockout Inference (GenKI), a virtual knockout (KO) tool for gene function prediction using single-cell RNA sequencing (scRNA-seq) data in the absence of KO samples when only wild-type (WT) samples are available. Without using any information from real KO samples, GenKI is designed to capture shifting patterns in gene regulation caused by the KO perturbation in an unsupervised manner and provide a robust and scalable framework for gene function studies. To achieve this goal, GenKI adapts a variational graph autoencoder (VGAE) model to learn latent representations of genes and interactions between genes from the input WT scRNA-seq data and a derived single-cell gene regulatory network (scGRN). The virtual KO data is then generated by computationally removing all edges of the KO gene-the gene to be knocked out for functional study-from the scGRN. The differences between WT and virtual KO data are discerned by using their corresponding latent parameters derived from the trained VGAE model. Our simulations show that GenKI accurately approximates the perturbation profiles upon gene KO and outperforms the state-of-the-art under a series of evaluation conditions. Using publicly available scRNA-seq data sets, we demonstrate that GenKI recapitulates discoveries of real-animal KO experiments and accurately predicts cell type-specific functions of KO genes. Thus, GenKI provides an in-silico alternative to KO experiments that may partially replace the need for genetically modified animals or other genetically perturbed systems.

A-LLTO Nanoparticles Embedded Composite Solid Polymer Electrolyte for Room Temperature Operational Li-metal Batteries.

Solid-state batteries (SSBs) have the potential to revolutionize the current energy storage sector. A significant portion of the current development of electric vehicles and the electrification of various appliances relies on Lithium (Li)-ion batteries. However, future energy demands will require the development of stronger and more reliable batteries. This report presents a novel solid state electrolyte (SSE) composed of a self-healing composite solid polymer electrolyte (CSPE) matrix and aluminum-doped (Li0.33La0.56)1.005Ti0.99Al0.01O3 (A-LLTO) nanofillers. The CSPE contains Jeffamine ED-2003 monomer, Benzene-1,3,5-tricarbaldehyde (BTC) crosslinker dissolved in a 1:1 ratio of Dimethylformamide (DMF) to LiPF6, and a certain amount (x) of A-LLTO nanofillers (x = 5, 7.5, 10, 12.5%). A CSPE containing x-amount of A-LLTO fillers (referred to as CAL-x%) demonstrates excellent ion-conducting properties and stable battery performance. The CAL-10% demonstrates 1.1 × 10-3 S cm-1 of ionic conductivity at room temperature (RT). A-LLTO nanofillers dispersed uniformly within the polymer matrix form a percolation network, which is believed to improve ionic conductivity and the diffusion of Li+ ions. The CR-2032 cell, consisting of LiFePO4 (LFP)║CAL-10%║Li, at RT offers an initial discharge capacity of ≈165 mAh g-1 at 0.1C rate for 120 cycles with 98.85% coulombic efficiency (C.E.).

Adipose lipolysis is important for ethanol to induce fatty liver in the National Institute on Alcohol Abuse and Alcoholism murine model of chronic and binge ethanol feeding.

BACKGROUND AND AIMS: Alcohol-associated liver disease (ALD) pathologies include steatosis, inflammation, and injury, which may progress to fibrosis, cirrhosis, and cancer. The liver receives ~60% of fatty acids from adipose tissue triglyceride hydrolysis, but the role of this lipolytic pathway in ALD development has not been directly examined in any genetic animal models with selective inactivation of adipose lipolysis. APPROACH AND RESULTS: Using adipose-specific comparative gene identification-58 (CGI-58) knockout (FAT-KO) mice, a model of impaired adipose lipolysis, we show that mice deficient in adipose lipolysis are almost completely protected against ethanol-induced hepatic steatosis and lipid peroxidation when subjected to the National Institute on Alcohol Abuse and Alcoholism chronic and binge ethanol feeding model. This is unlikely due to reduced lipid synthesis because this regimen of ethanol feeding down-regulated hepatic expression of lipogenic genes similarly in both genotypes. In the pair-fed group, FAT-KO relative to control mice displayed increased hepatocyte injury, neutrophil infiltration, and activation of the transcription factor signal transducer and activator of transcription 3 (STAT3) in the liver; and none of these were exacerbated by ethanol feeding. Activation of STAT3 is associated with a marked increase in hepatic leptin receptor mRNA expression and adipose inflammatory cell infiltration. CONCLUSIONS: Our findings establish a critical role of adipose lipolysis in driving hepatic steatosis and oxidative stress during ALD development.

Deep Learning Detection and Segmentation of Brain Arteriovenous Malformation on Magnetic Resonance Angiography.

BACKGROUND: The delineation of brain arteriovenous malformations (bAVMs) is crucial for subsequent treatment planning. Manual segmentation is time-consuming and labor-intensive. Applying deep learning to automatically detect and segment bAVM might help to improve clinical practice efficiency. PURPOSE: To develop an approach for detecting bAVM and segmenting its nidus on Time-of-flight magnetic resonance angiography using deep learning methods. STUDY TYPE: Retrospective. SUBJECTS: 221 bAVM patients aged 7-79 underwent radiosurgery from 2003 to 2020. They were split into 177 training, 22 validation, and 22 test data. FIELD STRENGTH/SEQUENCE: 1.5 T, Time-of-flight magnetic resonance angiography based on 3D gradient echo. ASSESSMENT: The YOLOv5 and YOLOv8 algorithms were utilized to detect bAVM lesions and the U-Net and U-Net++ models to segment the nidus from the bounding boxes. The mean average precision, F1, precision, and recall were used to assess the model performance on the bAVM detection. To evaluate the model's performance on nidus segmentation, the Dice coefficient and balanced average Hausdorff distance (rbAHD) were employed. STATISTICAL TESTS: The Student's t-test was used to test the cross-validation results (P < 0.05). The Wilcoxon rank test was applied to compare the median for the reference values and the model inference results (P < 0.05). RESULTS: The detection results demonstrated that the model with pretraining and augmentation performed optimally. The U-Net++ with random dilation mechanism resulted in higher Dice and lower rbAHD, compared to that without that mechanism, across varying dilated bounding box conditions (P < 0.05). When combining detection and segmentation, the Dice and rbAHD were statistically different from the references calculated using the detected bounding boxes (P < 0.05). For the detected lesions in the test dataset, it showed the highest Dice of 0.82 and the lowest rbAHD of 5.3%. DATA CONCLUSION: This study showed that pretraining and data augmentation improved YOLO detection performance. Properly limiting lesion ranges allows for adequate bAVM segmentation. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 1.

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Comparative efficacy of brolucizumab, half-dose photodynamic therapy, and aflibercept in managing chronic central serous chorioretinopathy.

PURPOSE: To compare the efficacy of brolucizumab, half-dose PDT, and aflibercept in treating chronic central serous chorioretinopathy (CSC). METHODS: A retrospective cohort study with chronic CSC patients who underwent intravitreal injection of one shot of brolucizumab or aflibercept in the first 3 months, followed by pro re nata regimens or a single session of half-dose PDT, was retrospectively reviewed. The primary outcome measure was the proportion of eyes that achieved complete absorption of retinal fluid without requiring any rescue treatment. Secondary outcomes included changes in best-corrected visual acuity (BCVA), central retinal thickness (CRT), and central choroidal thickness (CCT). RESULTS: A total of 54 consecutive patients were included in this study with 18 patients in each group. At months 1 and 2, the brolucizumab group exhibited the highest rate of complete retinal fluid resolution (61% and 77%), followed by the half-dose PDT group (56% and 72%), and lowest in the aflibercept group (28% and 33%), with statistically significant differences noted at month 2 (P = 0.012). The brolucizumab group also demonstrated the most significant reduction in CCT at months 1 and 2 among the three groups (P = 0.007 and 0.001). Recurrence of retinal fluid in the brolucizumab groups was predominantly observed at month 3. Conversely, the half-dose PDT group exhibited the most favorable anatomical results starting from month 3. Notably, mild vitritis was observed in one case from the brolucizumab group. CONCLUSIONS: Single injection of brolucizumab demonstrates trends of faster regression of persistent residual retinal fluid, greater CCT and CRT decline, and matched BCVA compared to half-dose PDT in the short term.

Anomalous vortex liquid in charge-ordered cuprate superconductors.

The interplay between charge order and d-wave superconductivity in high-[Formula: see text] cuprates remains an open question. While mounting evidence from spectroscopic probes indicates that charge order competes with superconductivity, to date little is known about the impact of charge order on charge transport in the mixed state, when vortices are present. Here we study the low-temperature electrical resistivity of three distinctly different cuprate families under intense magnetic fields, over a broad range of hole doping and current excitations. We find that the electronic transport in the doping regime where long-range charge order is known to be present is characterized by a nonohmic resistivity, the identifying feature of an anomalous vortex liquid. The field and temperature range in which this nonohmic behavior occurs indicates that the presence of long-range charge order is closely related to the emergence of this anomalous vortex liquid, near a vortex solid boundary that is defined by the excitation current in the [Formula: see text] 0 limit. Our findings further suggest that this anomalous vortex liquid, a manifestation of fragile superconductivity with a suppressed critical current density, is ubiquitous in the high-field state of charge-ordered cuprates.

Unconventional quantum vortex matter state hosts quantum oscillations in the underdoped high-temperature cuprate superconductors.

A central question in the underdoped cuprates pertains to the nature of the pseudogap ground state. A conventional metallic ground state of the pseudogap region has been argued to host quantum oscillations upon destruction of the superconducting order parameter by modest magnetic fields. Here, we use low applied measurement currents and millikelvin temperatures on ultrapure single crystals of underdoped [Formula: see text] to unearth an unconventional quantum vortex matter ground state characterized by vanishing electrical resistivity, magnetic hysteresis, and nonohmic electrical transport characteristics beyond the highest laboratory-accessible static fields. A model of the pseudogap ground state is now required to explain quantum oscillations that are hosted by the bulk quantum vortex matter state without experiencing sizable additional damping in the presence of a large maximum superconducting gap; possibilities include a pair density wave.

Identification of hyperthermophilic D-allulose 3-epimerase from Thermotoga sp. and its application as a high-performance biocatalyst for D-allulose synthesis.

D-Allulose 3-epimerase (DAE) is a vital biocatalyst for the industrial synthesis of D-allulose, an ultra-low calorie rare sugar. However, limited thermostability of DAEs hinders their use at high-temperature production. In this research, hyperthermophilic TI-DAE (Tm = 98.4 ± 0.7 ℃) from Thermotoga sp. was identified via in silico screening. A comparative study of the structure and function of site-directed saturation mutagenesis mutants pinpointed the residue I100 as pivotal in maintaining the high-temperature activity and thermostability of TI-DAE. Employing TI-DAE as a biocatalyst, D-allulose was produced from D-fructose with a conversion rate of 32.5%. Moreover, TI-DAE demonstrated excellent catalytic synergy with glucose isomerase CAGI, enabling the one-step conversion of D-glucose to D-allulose with a conversion rate of 21.6%. This study offers a promising resource for the enzyme engineering of DAEs and a high-performance biocatalyst for industrial D-allulose production.

Switchable metallacycles and metallacages.

Two-dimensional metallacycles and three-dimensional metallacages constructed by coordination-driven self-assembly have attracted much attention because they exhibit unique structures and properties and are highly efficient to synthesize. Introduction of switching into supramolecular chemistry systems is a popular strategy, as switching can endow systems with reversible features that are triggered by different stimuli. Through this strategy, novel switchable metallacycles and metallacages were generated, which can be reversibly switched into different stable states with distinct characteristics by external stimuli. Switchable metallacycles and metallacages exhibit versatile structures and reversible properties and are inherently dynamic and respond to artificial signals; thus, these structures have many promising applications in a wide range of fields, such as drug delivery, data processing, pollutant removal, switchable catalysis, smart functional materials, etc. This review focuses on the design of switchable metallacycles and metallacages, their switching behaviours and mechanisms triggered by external stimuli, and the corresponding structural changes and resultant properties and functions.

Parental Depression and Self-Stigma Among Chinese Young People Living With Depression: A Qualitative Study.

Self-stigma is detrimental to psychosocial well-being and the recovery journey among people living with depression. However, there has been limited research exploring the experience of stigma internalization when depression runs in families. This study aims to address this gap by (1) characterizing the manifestations of self-stigma among individuals living with depression whose parent(s) also have depression and (2) exploring the potential mechanisms underlying the impact of parental depression on self-stigma. Essential principles of the constructivist grounded theory approach were adopted to collect data through in-depth interviews with 27 participants aged 15-30, living in Mainland China. Many participants perceived depression running in their family as an endless disaster and an incurable illness. These beliefs further led to stigmatizing emotions (such as suppression, anger, and guilt) and behaviors (such as concealment and social withdrawal). Participants also highlighted ambivalent intergenerational relationships, tense family atmospheres, lower parental emotional involvement and support, and a lack of family flexibility due to parental depression. Furthermore, parental depression impacted participants' self-stigma by interfering with family relationships, family functioning, and parenting styles. It also shaped their perceptions of family, illness attribution, and public stigma. Additionally, parental depression had an impact on participants' social functioning, self-esteem, and personality, making them more susceptible to self-stigma. This study emphasizes the crucial role that the family plays in the internalization of stigma among individuals living with depression. It suggests that family dynamics, rather than family structure or economic backgrounds alone, shape this process.

Identification of Genetic Variants for Risk Prediction and Early Diagnosis of Age-Related Macular Degeneration in the Taiwanese Population.

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly worldwide. The prevalence and phenotypes of AMD differ among populations, including between people in Taiwan and other regions. We performed a genome-wide association study to identify genetic variants and to develop genetic models to predict the risk of AMD development and progression in the Taiwanese population. In total, 4039 patients with AMD and 16,488 non-AMD controls (aged ≥ 65 years) were included. We identified 31 AMD-associated variants (p < 5 × 10-8) on chromosome 10q26, surrounding PLEKHA1-ARMS2-HTRA1. Two genetic models were constructed using the clump and threshold method. Model 1 included the single nucleotide polymorphism rs11200630 and showed a 1.31-fold increase in the risk of AMD per risk allele (95% confidence interval (CI) = 1.20-1.43, p < 0.001). In model 2, 1412 single-nucleotide polymorphisms were selected to construct a polygenic risk score (PRS). Individuals with the top 5% PRS had a 1.40-fold higher AMD risk compared with that of individuals with a PRS in the bottom quartile (95% CI = 1.04-1.89, p = 0.025). Moreover, the PRS in the upper quartile was related to a decreased age at AMD diagnosis by 0.62 years (95% CI = -1.15, -0.09, p = 0.023). Both genetic models provide useful predictive power for populations at high risk of AMD, affording a basis for identifying patients requiring close follow-up and early intervention.

Mentorship for young gay men in Hong Kong: A pilot mixed-methods randomized controlled trial.

This study examined whether mentorship could promote young gay men's identities and well-being, and whether a mentor's sexual orientation matters. A randomized control trial compared outcomes across three conditions: Arm A (a mentee matched with a sexual minority mentor), Arm B (a mentee matched with a heterosexual mentor), and a control arm receiving psychoeducation only. A community sample of 60 mentees aged 18-25 years was randomly allocated to the three arms and completed questionnaires at baseline, 3 months into the intervention, and at the end of the 6-month program. Fifteen mentees recounted their mentoring experiences through in-depth interviews. Linear mixed effects models showed that for both intervention and control conditions, internalized homonegativity declined while resilience, loneliness, and body acceptance improved over time. No time and group interactions were found. Meanwhile, a mentor's sexual orientation did not drive differential quality and outcomes of mentorship. Interviewees cited various benefits of mentorship, including providing companionship, enriching connection with lesbian, gay, bisexual (LGB) communities, and adding knowledge and perspectives of LGB lives and identities. Although quantitative data did not support any exclusive benefits of mentorship, most mentees recognized mentorship as a vital source of affirmation and companionship. Implications for research and mentoring programs are discussed.

Phonological processing in Chinese word repetition: Dementia effect and age effect.

This paper examined phonological processing as reflected in Mandarin word and non-word repetition. Issues related to dementia effects, age effects, and linguistic variables are discussed. Forty-five speakers of Mandarin Chinese in Taiwan took part in this study. Fourteen seniors with Alzheimer's disease (AD), 14 healthy adults, and 17 healthy seniors individually completed a task of word repetition. Scores of accurate repetition were calculated, and error types as well as error frequency were analysed. Linguistic variables, including lexicality, frequency, non-word, and syllable length, were discussed. Major findings were indicative of a significant dementia effect, in which participants with AD were significantly impaired in repeating words with more syllables, specifically 5-syllable non-words of low frequency. Numerous significant age effects were noted. Theoretical implications and directions for future study are discussed.

Federated Learning: A Cross-Institutional Feasibility Study of Deep Learning Based Intracranial Tumor Delineation Framework for Stereotactic Radiosurgery.

BACKGROUND: Deep learning-based segmentation algorithms usually required large or multi-institute data sets to improve the performance and ability of generalization. However, protecting patient privacy is a key concern in the multi-institutional studies when conventional centralized learning (CL) is used. PURPOSE: To explores the feasibility of a proposed lesion delineation for stereotactic radiosurgery (SRS) scheme for federated learning (FL), which can solve decentralization and privacy protection concerns. STUDY TYPE: Retrospective. SUBJECTS: 506 and 118 vestibular schwannoma patients aged 15-88 and 22-85 from two institutes, respectively; 1069 and 256 meningioma patients aged 12-91 and 23-85, respectively; 574 and 705 brain metastasis patients aged 26-92 and 28-89, respectively. FIELD STRENGTH/SEQUENCE: 1.5T, spin-echo, and gradient-echo [Correction added after first online publication on 21 August 2023. Field Strength has been changed to "1.5T" from "5T" in this sentence.]. ASSESSMENT: The proposed lesion delineation method was integrated into an FL framework, and CL models were established as the baseline. The effect of image standardization strategies was also explored. The dice coefficient was used to evaluate the segmentation between the predicted delineation and the ground truth, which was manual delineated by neurosurgeons and a neuroradiologist. STATISTICAL TESTS: The paired t-test was applied to compare the mean for the evaluated dice scores (p < 0.05). RESULTS: FL performed the comparable mean dice coefficient to CL for the testing set of Taipei Veterans General Hospital regardless of standardization and parameter; for the Taichung Veterans General Hospital data, CL significantly (p < 0.05) outperformed FL while using bi-parameter, but comparable results while using single-parameter. For the non-SRS data, FL achieved the comparable applicability to CL with mean dice 0.78 versus 0.78 (without standardization), and outperformed to the baseline models of two institutes. DATA CONCLUSION: The proposed lesion delineation successfully implemented into an FL framework. The FL models were applicable on SRS data of each participating institute, and the FL exhibited comparable mean dice coefficient to CL on non-SRS dataset. Standardization strategies would be recommended when FL is used. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 1.

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.

BACKGROUND: Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery. METHODS: Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed. RESULTS: AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application. CONCLUSIONS: Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.

The association of potentially inappropriate medications, polypharmacy and anticholinergic burden with readmission and emergency room revisit after discharge: A hospital-based retrospective cohort study.

AIMS: While certain drug-use indicators are known to be associated with clinical outcomes, the relationship is unclear for some highly prevalent conditions in in patients aged ≥65 years. We examine correlations between 3 drug-use indicators and postdischarge healthcare services use by older patients according to the presence of dementia, advanced age and frailty. METHODS: This retrospective cohort study analysed data collected from hospital electronic health records between April and December 2017. Potentially inappropriate medications (PIMs) and anticholinergic burden were assessed using the 2015 Beers Criteria and anticholinergic cognitive burden scale (ACBS) score. Minor and major polypharmacy were defined as the use of 5-9 and ≥10 drugs, respectively. Outcomes were set as emergency room revisits and readmissions at 1, 3 and 6 months postdischarge. The correlation between drug-use indicators and outcomes was analysed by multivariable logistic regression. RESULTS: The final cohort included 3061 patients for the analysis, and 2930, 2671 and 2560 patients were followed up to 1, 3 and 6 months after discharge. After controlling for confounders, all 3 drug-use indicators were significantly associated with readmission and emergency room revisits except for the relationship between PIMs and readmission within 6 months. These associations were significantly observed among patients without dementia, aged >80 years and with frailty. CONCLUSION: PIMs, polypharmacy and anticholinergic burden are common at discharge and correlate with future use of healthcare services. In older patients, the absence of dementia, advanced age and frailty should be given extra consideration with regard to medication safety.

Prediction of individual trigeminal pain sensitivity from gray matter structure within the sensorimotor network.

OBJECTIVE: To determine whether multivariate pattern regression analysis based on gray matter (GM) images constrained to the sensorimotor network could accurately predict trigeminal heat pain sensitivity in healthy individuals. BACKGROUND: Prediction of individual pain sensitivity is of clinical relevance as high pain sensitivity is associated with increased risks of postoperative pain, pain chronification, and a poor treatment response. However, as pain is a subjective experience accurate identification of such individuals can be difficult. GM structure of sensorimotor regions have been shown to vary with pain sensitivity. It is unclear whether GM structure within these regions can be used to predict pain sensitivity. METHODS: In this cross-sectional study, structural magnetic resonance images and pain thresholds in response to contact heat stimulation of the left supraorbital area were obtained from 79 healthy participants. Voxel-based morphometry was used to extract segmented and normalized GM images. These were then constrained to a mask encompassing the functionally defined resting-state sensorimotor network. The masked images and pain thresholds entered a multivariate relevance vector regression analysis for quantitative prediction of the individual pain thresholds. The correspondence between predicted and actual pain thresholds was indexed by the Pearson correlation coefficient (r) and the mean squared error (MSE). The generalizability of the model was assessed by 10-fold and 5-fold cross-validation. Non-parametric permutation tests were used to estimate significance levels. RESULTS: Trigeminal heat pain sensitivity could be predicted from GM structure within the sensorimotor network with significant accuracy (10-fold: r = 0.53, p < 0.001, MSE = 10.32, p = 0.001; 5-fold: r = 0.46, p = 0.001, MSE = 10.54, p < 0.001). The resulting multivariate weight maps revealed that accurate prediction relied on multiple widespread regions within the sensorimotor network. CONCLUSION: A multivariate pattern of GM structure within the sensorimotor network could be used to make accurate predictions about trigeminal heat pain sensitivity at the individual level in healthy participants. Widespread regions within the sensorimotor network contributed to the predictive model.

Increased risk of glaucoma development in patients with central serous chorioretinopathy: results of a 11-year population-based cohort study.

PURPOSE: To investigate whether patients with central serous chorioretinopathy (CSC) have increased risk of developing glaucoma. METHODS: Patients diagnosed with CSC between 1 January 2008 and 31 December 2018 were included in this study using data from the Taiwanese National Health Insurance Research Database (NHIRD). The CSC cohort was matched with a non-CSC cohort using the propensity score matching method, based on sex, age (in 10-year intervals), index date year, comorbidities, and steroid use, resulting in equal numbers of patients in both cohorts. Patients were followed up until 31 December 2019 or until they were withdrawn from the NHIRD. The incidence of glaucoma was compared between the two cohorts using the Cox regression model, and the risk of developing glaucoma was estimated using the Kaplan-Meier method. RESULTS: After adjusting for sex, age, comorbidities, and steroid use, the CSC cohort showed a significantly higher risk of developing glaucoma compared to those without CSC (adjusted HR = 3.99; 95% CI = 3.44-4.62). The cumulative incidence of glaucoma in the CSC cohort was also significantly higher than in the non-CSC cohort (log-rank test, p < 0.001). Among the glaucoma subtypes, normal tension glaucoma had the highest risk (adjusted HR = 5.79; 95% CI = 3.41-9.85), followed by primary open-angle glaucoma (adjusted HR = 2.77; 95% CI = 2.12-3.62). CONCLUSIONS: In conclusion, our study shows that CSC patients are at a higher risk of developing glaucoma, especially NTG. Awareness and regular glaucoma screenings are essential for patients with CSC.